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brain
sciences
Review
How Human Single-Neuron Recordings Can Help Us
Understand Cognition: Insights from Memory Studies
Zuzanna Roma Kubska * and Jan Kamiński
Center of Excellence for Neural Plasticity and Brain Disorders: BRAINCITY, Nencki Institute of Experimental
Biology, Polish Academy of Sciences, 02-093 Warsaw, Poland; j.kaminski@nencki.edu.pl
* Correspondence: z.kubska@nencki.edu.pl
Abstract: Understanding human cognition is a key goal of contemporary neuroscience. Due to the
complexity of the human brain, animal studies and noninvasive techniques, however valuable, are
incapable of providing us with a full understanding of human cognition. In the light of existing cogni-
tive theories, we describe findings obtained thanks to human single-neuron recordings, including the
discovery of concept cells and novelty-dependent cells, or activity patterns behind working memory,
such as persistent activity. We propose future directions for studies using human single-neuron
recordings and we discuss possible opportunities of investigating pathological brain.
Keywords: human single-neuron recordings; cognition; concept cells; long-term memory; working
memory; persistent activity
1. Introduction
Citation: Kubska, Z.R.; Kamiński, J. The way the human brain explores the world, acquires knowledge and processes
How Human Single-Neuron information obtained through the senses has always been a mystery and a point of interest
Recordings Can Help Us Understand for philosophers and scientists alike. The last decades brought an abundance of research on
Cognition: Insights from Memory cognitive processes, such as attention, memory, perception or executive functions, leading
Studies. Brain Sci. 2021, 11, 443. to many important and critical cognitive theories, some of which were divergent or even
https://doi.org/10.3390/
contradictory. Theories on working memory (WM) come to mind as an example. The
brainsci11040443
widely known Baddeley and Hitch’s model divides WM into different, domain-specific
(that is related to the sensory domain) components, while Cowan’s model focuses on
Academic Editor: Leszek Kaczmarek
attentional processes [1,2]. Although Cowan does not deny the existence of domain-
specific stores in WM, he favors domain-general approach, especially when it comes to the
Received: 28 February 2021
Accepted: 26 March 2021
maintenance mechanism of WM [3]. According to this approach, regardless of the sensory
Published: 30 March 2021
domain, neuronal networks maintain information using the same mechanism. Cowan
also states that there are 4 items/chunks held in focus of attention, however in Oberauer’s
Publisher’s Note: MDPI stays neutral
model this number is limited to 1 [4]. Who, then, is right?
with regard to jurisdictional claims in
Although the question is simple, the answer is not. Cognitive neuroscientists have
published maps and institutional affil- tried to address this and other questions, and figure out how the human brain works using
iations. different neuroimaging techniques, e.g., fMRI, EEG, MEG. These noninvasive techniques,
however, come with many limitations. There is always a trade-off between spatial and time
resolution [5]. EEG (or MEG) has very good time resolution, but provides limited insights
about the locus of activity due to the inverse problem. fMRI, on the other hand, shows the
locus of activity, but we can only infer about the neural activity from the blood-oxygen level
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
dependent (BOLD) signal and, therefore, know little about the nature of neuronal activity:
This article is an open access article
whether it is inhibitory or excitatory. Moreover, fMRI has poor time resolution [6–8].
distributed under the terms and
2. Human Single-Neuron Recordings
conditions of the Creative Commons
Attribution (CC BY) license (https:// Due to the limitations of noninvasive techniques and the fact that—despite an abun-
creativecommons.org/licenses/by/ dance of methods—little progress in solving the mystery of human cognition has been
4.0/). made to date [9], researchers resort to invasive techniques of recording brain activity, such
Brain Sci. 2021, 11, 443. https://doi.org/10.3390/brainsci11040443 https://www.mdpi.com/journal/brainsci
Brain Sci. 2021, 11, 443 2 of 16
as human single-neuron recordings. This technique has perfect spatial and time resolution,
and although some limitations should be taken into account [10], it provides direct evidence
of how the human brain works on cellular level and insights that would be impossible
to obtain otherwise. Studies that led to the discovery of concept cells, that is cells that
respond to the concept regardless of the way of presentation (auditory cues or different
images), may serve as example [11,12]. What is even more important, researchers are able
to use only verbal instructions and more advanced tasks than it is possible in studies on
animals, which often require long days of training and use of simple tasks. We discuss
concept cells and other discoveries further in this review, but for now we would like to
stress that studies on humans using human single-neuron recordings gave us invaluable
and otherwise unobtainable insights into our cognitive processes, such as memory, and the
way the human brain processes information [10].
The possibility to record single neurons in humans comes from medical procedures in
which implantation of the electrode into the human brain is a necessary part of the treat-
ment. Currently, the bulk of research in this field comes from invasive epilepsy monitoring
using the so-called “Behnke-Fried” electrodes [13]. Introduced in the late 1990s, these
electrodes combine depth electrodes normally used for clinical purposes with microwires
protruding from the end of the depth electrodes. Each electrode contains a set of eight
microwires capable of recording single neurons. The placement of deep brain stimulation
(DBS) electrodes in Parkinson’s or other diseases provides another opportunity to record
neuronal activity in the human brain. In this procedure, neurosurgeons often use micro-
electrodes in order to improve the targeting of the DBS electrode, which makes it possible
to record single-neuron activity along the trajectory of electrode implantation [14–17]. Re-
search focusing on Brain Computer Interfaces (BCI), where electrodes implanted into the
brain are being used, is yet another way we can record neuronal activity. Although the
main goal of these projects is to create effective interfaces for paralyzed subjects, some
of the experiments also deepen our knowledge of neuronal mechanism of cognitive sys-
tem [18,19].
In all of the situations described above, using high impedance electrodes, we are able
to record extracellularly action potentials of neurons in the near vicinity of the electrode.
Next, using the procedure of spike sorting, we can extract the activity of putative neurons
in human brain [20]. In the following paragraphs, we analyze some concepts formulated
or confirmed thanks to this method, and analyze some of the conceptual frameworks of
cognition from human single-neuron recordings perspective.
3. Grandmother, Gnostic and Concept Cells
Theories stating that relatively small groups of neurons, or—in extreme cases—even
single neurons, code particular percepts in the brain date back to the 1960s, when two
neuroscientists, Konorski and Lettvin, came up with two quite similar ideas almost simul-
taneously. Lettvin postulated that there are cells that respond to a picture of somebody
familiar, e.g., the image of one’s grandmother, hence the name “grandmother cell” [21,22].
Konorski, on the other hand, proposed that there are cells (or cell assemblies) in the higher
levels of cortical hierarchy that respond to complex stimuli and are responsible for coding
object categories [23]. He called them “gnostic” cells. Both hypotheses were hard to verify
experimentally until sparse, yet specific, responses of neurons in the medial temporal
lobe (MTL) were discovered [12]. Thanks to human single-neuron recordings in epileptic
patients implanted with depth electrodes, cells that responded to certain concepts were
discovered. One of the first of such neurons was called the Jennifer Aniston neuron, as
it responded selectively to different pictures of the actress Jennifer Aniston [12,24]. More
specifically, it did not respond when the actress was depicted standing next to her former
husband, Brad Pitt, yet responded to the image of her Friends costar Lisa Kudrow, even
though Aniston was not present in the picture. This led to the conclusion that this particular
neuron was responding not to the image of the actress herself, but probably to the concept
of TV series “Friends”: as these two concepts (two actresses starring in the same series) were
Brain Sci. 2021, 11, 443 3 of 16
related semantically, they probably activated the same neuron [25]. Moreover, as De Falco
and colleagues [25] demonstrated, such associations outstand task performance, therefore
they are associated with neuronal mechanisms related to long-term memory. Diffused
topography of responses enables associative learning and forming associations between
distinct, visually unrelated concepts. The discovery was a step towards understanding
neural mechanisms of abstract thoughts, something unique to humans. Similar results
were found in a study where a cell that responded to Luke Skywalker also responded to
the picture of Yoda, another “Star Wars” character [11]. Moreover, it was discovered that
concept cells reacted selectively to certain visual stimuli even though they were linked only
semantically and not visually (e.g., not only different pictures of a person, but also pencil
sketches of them, or their name written down) [11]. That led to a conclusion that, unlike the
grandmother or gnostic cells, these neurons fire to the semantic content and the concept of
a person, not visual similarity. This finding was further supported by a study that showed
that neurons responded to both visual and auditory stimuli referring to the same person
(e.g., spoken or written names) [11]. In addition, it was discovered that the higher the level
of hierarchical structure of MTL, the stronger the visual and multimodal invariance, which
could mean that the higher level of organization, the higher the degree of abstraction [10].
The study also found a neuron that responded to both the Sydney Opera House and Bahai
Temple in India (same patient whose other neuron responded to Jennifer Aniston). As it
was later confirmed by the patient, he did not distinguish between these two buildings,
which explains the firing of one neuron in response to two different stimuli. Note, that this
insight would not be possible in animal studies, as feedback from the patient was crucial to
the interpretation of this neural activity, which is a significant advantage of single-neuron
recordings in human subjects.
These studies and another study by Quian Quiroga and colleagues [26] show that
concept cells do not signal visual features of stimuli, but subjects’ perceptual decisions
related to awareness. In the study mentioned [26] the subject was presented morphed,
ambiguous pictures of two familiar faces (e.g., Bob Marley and Whoopi Goldberg), whereas
a neuron that fired to one of them (Whoopi Goldberg) and not to the other (Bob Marley)
was previously found. When the subject recognized Whoopi Goldberg’s morphed picture
the stimulus-specific neuron responded strongly, which was not the case when the subject
recognized Bob Marley. What is more, a linear classifier could predict the subject’s decision.
Therefore, it is the decision and recognition (or categorization) of the stimulus that this MTL
neuron fired to and not to visual features, and therefore this activity reflected consciousness.
Moreover, research conducted by Reber and colleagues [27] using single-neuron recordings
suggests gradual rather than all-or-nothing way of neural mechanisms of conscious per-
ception as MTL stimulus-specific cells react to preferred stimulus even without conscious
perception of the subject. What is even more important in this study, the higher up in the
MTL hierarchy, the more neuronal response seems to be related to consciousness in terms
of precise timing and magnitude, with anterior regions of MTL being the most prominent
for conscious perception.
The discovery of concept cells is a chief example of the value of human single-neuron
recordings. Those neurons were discovered in humans and most likely are unique to
humans, as to date these specific cells were not found in other species [10], whose neurons
seem to change their firing with contextual changes—unlike concept cells. The discovery of
concept cells proved that at the end of the visual stream we can observe highly specialized
cells which respond to high level features, as theorized in the previous century. Yet, cells
recorded in MTL respond to visual stimulus as well as multimodal concepts. Those cells
are also an invaluable tool for probing human cognition, allowing us to test conscious ex-
perience [26,27], organization of human knowledge [24,25], but also neuronal mechanisms
of long-term memory or working memory [12,18,28–30], as explained towards the end of
this review.Brain Sci. 2021, 11, 443 4 of 16
4. Insights from Human Single-Neuron Recordings to Long-Term Memory (LTM)
The role of MTL in memory processes is well documented. Observations of patients
after lesions showed that this particular region plays a substantial role in forming new
memories, successful consolidation and recall [31–33]. As the highest structure in the hier-
archy of MTL’s organization, the hippocampus is involved in episodic memory [34,35] and
association making [36]. As concept cells were discovered in MTL, they were considered in
light of the role they can play in memory processes.
Our brain creates memories by making associations [37]. That is why we better
remember things that are related to places we know and people we love. It is a well-known
fact that it is easier to recall things in the place or circumstances they were learnt. One
stimulus may trigger another if they were encoded together, are similar to each other,
or our brain made an association/connection between them. This well observed effect
was recently supported by a study that used human single-neuron recordings [28,38].
Once Ison and colleagues [28] found concept cells that responded to particular stimuli,
they presented photoshopped pictures that contained these stimuli with a new one. For
example, if a neuron originally responded to Jennifer Aniston and not the Eiffel Tower,
the picture of the actress was photoshopped to show her standing near the Eiffel Tower.
It turned out that after several expositions, the cell originally responding to Jennifer
Aniston’s picture responded to the Eiffel Tower as well. Clearly, an association was
made, and the neuron started to respond to both. This finding may mean that neurons
in MTL play an important role in categorizing new information and integrating it with
already existing concepts and memories. Possibly, concept cells are not units that form
memory traces or stable representations of percepts per se, but are crucial for information
encoding and consolidation processes. This view is consistent with studies on patients
with lesions in MTL and their ability to remember old memories and difficulty in forming
new ones [32,39]. Quian Quiroga, however, goes further with his interpretation and claims
that categorization may happen in other brain areas and concept cells fire when conscious
perception of stimulus occurs [24]. According to this theory, a concept cell brings stimulus
into awareness, which enables association making and forming new memories in the flow
of consciousness. Indeed, as described in the previous section, precise and stereotypical
response of concept cells is associated with conscious experience [27].
Yet, concept cells are not the only finding that provided insights into cognitive pro-
cesses thanks to human single-neuron recordings. Lisman and Grace proposed a theory of
a functional loop between the hippocampus and substantia nigra/ventral tegmental area
(SN/VTA) that is responsible for detecting novelty/familiarity and controlling the entry
of information to LTM [40,41]. SN and VTA are key dopaminergic regions in the human
midbrain. Firstly, novelty signals detected by the hippocampus are transferred to SN/VTA,
where novelty-dependent cells are activated. Meanwhile, basal ganglia receive inputs from
the prefrontal cortex (PFC) signaling top-down attention and motivation. Based on all
the inputs, the enhancing/weakening of information occurs. Finally, feedback dopamine
released to the hippocampus enhances long-term potentiation (LTP), which opens the
entry to LTM and enables successful memorization [40,41]. Studies using human single-
neuron recordings provided evidence in line with this theory. Researchers discovered
populations of neurons in MTL that responded to the novelty vs. familiarity of stimulus
with no regard to the category or visual features of the presented object [42–44] (Figure 1).
Making a distinction between known and unknown information is crucial for memory. In
other words, these neurons seem to “tell the brain” which stimuli are new and need to be
memorized. What is interesting, similar neurons were found in the substantia nigra (SN)
as well [16]. An analysis of action potentials and their characteristics led to the conclusion
that these were probably dopaminergic neurons, and we already know dopamine plays
a crucial role in cognitive processes such as attention and memory [40,41,45]. The study
showed as well that the neurons that responded to novel stimuli in SN had longer response
latency than similar cells in MTL. The difference in latencies may mean that after “the
first screening for novelty” that occurs in the hippocampus, the information proceedsBrain Sci. 2021, 11, 443 5 of 16
then to the basal ganglia (and SN), where it is further reinforced or weakened depending
on one’s top-down attention and goals. These findings are coherent with Lisman’s and
Grace’s concept of dynamic hippocampal-SN/VTA loop and its inputs from PFC. More-
over, Rutishauser and colleagues [18] using single-neuron recordings found two groups
of neurons in the posterior parietal cortex (PPC) that signaled memory-based choices: a
group of memory-selective novelty-dependent neurons (Figure 1) and confidence-selective
neurons that differentiated between high- and low-confidence responses. The response of
the former group of neurons was a function of confidence (subjective, regardless of truth),
while the latter group signaled confidence independently of the novelty factor. This finding
provides direct evidence for the involvement of PPC in declarative memory processes, such
as transforming memories (knowledge) into choices based on confidence.
Figure 1. The figure shows two types of brain cells found using human single-neuron recordings:
concept cells and novelty-dependent cells. Concept cells response in medial temporal lobe (MTL).
Concept cells are activated by stimuli that form a part of the same concept and are associated
semantically, but have no common visual features (e.g., concept: “Italy”; stimuli: country outline,
pizza, wine), regardless of sensory modality, for instance an audio stimulus. The cell is not activated
by stimuli if they form a part of a different concept (e.g., concept: “USA”) or are associated with
stimuli that do not form a part of the “Italy” concept (e.g., pizza and the American flag) [24]. Novelty-
dependent cells were found in the hippocampus, substantia nigra (SN) and posterior parietal cortex
(PPC) [16,42–44]. Initial screening for novelty occurs in the hippocampus and it is further reinforced
(or weakened) in SN and basal ganglia, depending on inputs from frontal cortex (FC) signaling
top-down attention and one’s goals. Differentiating between the novelty vs. familiarity of a stimulus
informs the brain if the stimulus is new and should be memorized. The functional hippocampal-
SN/ventral tegmental area (VTA) loop and feedback dopamine release enhances LTP and enables
successful memorization. Novelty-dependent memory-selective neurons in PPC play a role in
memory-based choices.
Humans have an exceptional ability to memorize new items. We can learn more
than 2000 images after short exposition over a period of several days [46]. Studies using
human single-neuron recordings described in this section helped to provide evidence for
theoretical frameworks of this process, and revealed the nature of particular cells andBrain Sci. 2021, 11, 443 6 of 16
their role in memory building: from categorization (concept cells) [24], to memorization
process [16,42–44] (novelty-dependent neurons and dopamine secretion) to memory-based
choices [18] (Figure 1).
5. Insights from Human Single-Neuron Recordings to Working Memory (WM)
As pointed out above, studies using human single-neuron recordings provided in-
sights into the role of MTL in human long-term memory, showing the involvement of
concept cells in association making and encoding, as well as finding neurons that distin-
guish between novel vs. familiar stimuli. Encoding new stimuli does not, however, occur
only in long-term memory itself and is strictly related to WM and attention [2,4,47,48]. As
suggested in Atkinson and Shiffirin’s model of long-term memory [47], working memory
(called short-term storage) acts as a storage that transfers attended stimuli to long-term
memory and receives input from LTM as well. WM is an important cognitive process
that mediates other, more advanced cognitive functions, such as reasoning, calculation or
problem solving [49]. Being so, human studies are irreplaceable as many of these processes
are unique to human behavior and it is impossible to infer about them from animal stud-
ies. However, what can we learn about these aspects of cognition by analyzing human
single-neuron recordings?
Animal studies set out the course for investigating human WM. Persistent activity (PA)
recorded in animal brains made researchers suspect it could potentially be a mechanism
for maintaining items in human WM [50,51]. Additionally, what is persistent activity? It
occurs when neurons that responded to a stimulus during encoding keep on firing during
the maintenance period when the stimulus is no longer present. It is supposed that the
mechanism enables the brain to hold information in WM after a stimulus disappears. As
human WM is far more complex than animal WM, scientists asked: is the same mechanism
present in the human brain?
Addressing the question became possible thanks to human single-neuron record-
ings. First evidence of persistent activity (PA) in human working memory was found by
Kamiński and colleagues [29] almost simultaneously with Kornblith and colleagues [30]
(Figure 2). Both studies were carried out on epileptic patients with depth electrodes im-
planted in MTL and both provided evidence that made it possible to consider persistent
activity as a neural mechanism for WM. In both studies, neuronal activity during the
maintenance period predicted behavioral outcome. Once Kamiński and colleagues identi-
fied concept cells, they showed that these cells continued their activity for a few seconds
throughout the maintenance period after the stimulus offset. Similar neuronal activity was
found in Kornblith and colleagues’ study, and although the number of neurons was small,
their PA was highly significant and behaviorally relevant. This is an important finding as—
with the number of neurons being small and their distribution sparse [24,52]—PA would
probably be impossible to observe with noninvasive techniques and it is a unique insight
from human single-neuron studies that would not be possible to obtain otherwise. More-
over, behavioral performance could be predicted based on the strength of stimulus-specific
neuronal activity, which decreased as a function of working memory load [29].
These findings are in line with a view based on animal studies which states that PA
is an important mechanism for WM [51,53,54]. They also provide first direct evidence for
PA as a potential mechanism for WM maintenance in humans, however, at first they may
seem incoherent with the fact that MTL is mainly considered to be a crucial structure for
LTM. Although some patients with MTL lesions are as good as healthy controls when it
comes to performing a WM task [32], their performance of more demanding tasks (longer
maintenance time, higher load, or distractors) is impaired [55]. As WM seems to have a
more distributed representation in the brain [56], a number of areas need to act in concert
in order to perform WM tasks, especially the more demanding ones.Brain Sci. 2021, 11, 443 7 of 16
Figure 2. Persistent activity of concept cell (stimulus specific cell) in MTL and maintenance neu-
rons (non-stimulus-specific cell) in medial frontal cortex (MFC) during working memory (WM)
maintenance. A concept cell stays active during maintenance if preferred stimulus was present
during encoding, yet activity decreases as a function of load. Persistent activity of the concept cell
during maintenance correlates with the behavioral outcome (correctness) [29,30]. Persistent activity
of a maintenance neuron decreases as a function of load. Its persistent activity correlates with the
behavioral outcome (correctness and speed) [29].
What is more, combining human single-neuron recordings with other methods pro-
vides unique insights. It also makes it possible to link specific single-unit activity with the
activity of bigger neuronal populations from other regions and infer about their relation.
For instance, Kamiński and colleagues [57] showed that the phase of slow oscillation in
which a neuron fires can be used to read out content of WM (phase coding). In a different
study Boran and colleagues [58] analyzed human single-neuron recordings from MTL
together with iEEG in MTL and scalp EEG. They proved that persistent activity recorded in
the hippocampus was involved in working memory processing, predicted workload during
maintenance and showed that coupling between scalp and hippocampal EEG increased
with higher workload. This finding supports the hypothesis about distributed organiza-
tion of WM [56] and may lead to the conclusion that full WM capability is only possible
when different regions connect and act in concert. (Which may explain why patients with
LTM lesions perform well in simple WM tasks and show impaired WM functions in more
demanding tasks, as mentioned earlier).
The precise capacity of WM remains an open question. Researchers cannot agree if the
number is 7+/−2 [59], 4 as in Cowan’s model [60] or 1 as suggested by Oberauer [4], but
studies based on human single-neuron recordings moved closer to finding the answer to
that question. As shown, the frontal cortex (dACC and preSMA) is a region that contains
maintenance neurons which show load-related activity and are independent of stimulus
content unlike concept cells [29] (Figure 2). As the authors argue, since medial frontal
cortex (MFC) lesions resulted in impaired switching between task sets [61], changes in load-
dependent activity of these neurons and in RT may be due to different attentional demand
and implementation of task sets, therefore these neurons may be related to WM’s central
executive component, as they may inform what the current task requirements are (different
at every stage of the task: encoding, maintenance or retrieval) [54]. Load-dependent
neurons were found in the hippocampus as well [58], although finding these neurons inBrain Sci. 2021, 11, 443 8 of 16
different brain areas may be due to different tasks and therefore different WM maintenance
mechanisms involved (load-dependent vs. complex concept-dependent maintenance).
If so, in order to estimate WM capacity, we may first need to determine these types of
maintenance mechanisms.
To sum up, human WM is a complex cognitive process with distributed organization
that differs a lot in its complexity as compared to animal WM. It gives rise to more advanced
processes, such as reasoning or problem solving, which are unique to humans. Although
invasive animal studies provide undoubtedly useful insights into neural correlates and
the mechanisms of WM, they are not sufficient due to the complexity of human behavior.
Animal studies with small loads, long hours of training and no feedback information from
subjects do not shed light on the complexity of human cognition. Using human single-
neuron recordings, we found direct evidence that non-stimulus-selective cells in human
MFC are involved in successful WM performance and may be related to attentional control.
We may also assume that similar cells in different regions of the brain may contribute to
maintenance mechanisms, yet support different aspects of it.
6. How Human Single-Neuron Recordings Can Help Us Understand Cognition
There are many questions about neuronal mechanisms of human cognition that remain
open. Due to technical limitations and ethical issues related to human single-neuron
recordings, researchers cannot freely design cognitive studies. Therefore, it is important
to carry out well designed studies that address questions which are basic, yet crucial for
further scientific development. In this review, we described some major achievements on
our quest to understand human cognition obtained via human single-neuron recordings.
Over the last two decades, the number of studies using human single-neuron recordings
almost doubled (Figure 3).
Figure 3. Increase in number of publications with phrase “human single-neuron recordings”. Data
from Pub-Med.
Nevertheless, this is just the starting point and many questions still await their answers.
Future directions for studies using human single-neuron recordings may be considered
from three different perspectives: (1) the kind of cognitive theories process investigated
(and their aspects); (2) the location of electrodes (brain area); (3) the technology used.
The most important direction for cognitive studies using human single-neuron record-
ings are areas where the human mind is different from the mind of other mammals studied
by neuroscience, such as the theory of mind, inference, language or working memory.Brain Sci. 2021, 11, 443 9 of 16
Here, we will describe a few examples where this technique could help us to further our
understanding of working memory.
Understanding the capacity of working memory, or the number of chunks of informa-
tion we can hold in the focus of attention, will not only show us the capabilities of human
mind, but will also support a given working memory model. As we mentioned earlier in
this review, researchers cannot agree on the limits of WM capacity [4,59,60]. Many propose
a different approach to the mechanism of working memory. Single-neuron recordings can
make it possible to test WM capacity. For instance, researchers observed a decrease in
stimulus-specific persistent activity with load [29] and no relation between the activity and
position of the stimulus in the stream [30]. It is possible that when persistent activity is
no longer distinguishable from the baseline activity load marks the capacity of working
memory. Alternatively, we may be able to estimate WM capacity by counting cells that
code individual stimuli in a unit of time determined by LFP oscillatory activity [62]. If
we found that neurons which code different memoranda never fire together during WM
task performance, we could assume that the number of information chunks in the focus of
attention is limited to 1, as Oberauer predicted [4]. We might also look for differences in
activity magnitude, assuming that the bigger the magnitude, the more attention is allocated
to that memorandum and that memoranda with similar magnitude are probably at the
same level of activation.
When it comes to WM maintenance, it also remains unclear whether the domain-
specific or the domain-general approach is accurate [1–3]. However, we may address this
question using human single-neuron recordings, by asking patients to memorize items
represented in different modalities to test if those modalities are represented in persistent
activity of the same cells. If so, it would be evidence in favor of the domain general
approach; if not, it would mean that there actually are separate cellular storages for stimuli
of different modality, like Baddeley and Hitch predicted. Preferably, while analyzing the
outcomes, we should also bear in mind that different WM maintenance mechanisms may be
involved (load- and complex concept-dependent maintenance) [29,58]. Maintaining stimuli
in memory is one thing, but what is specific to human cognition is making operations on
them. For years, the hypothesis of mental high-speed serial exhaustive scanning process
(SES) was widely investigated and found confirmation in many studies (see [63]), but some
researchers have their doubts, e.g., [64–66]. What is interesting in SES is that reaction time
increases with load equally for both positive and negative trials. Supposedly, once a search
for a probe is started, all items in the set are being scanned. It seems counterintuitive as
the search should stop once an item is found and there is no need for further scanning.
In order to check that on the cellular level, we could infer about SES existence from the
activity of cells that code individual stimuli: cells’ response time positively correlated with
load would be an argument in favor of SES.
The location of electrode implementation is predefined by surgical prerequisites,
therefore, it cannot be manipulated freely. A vast majority of cognitive single-unit studies
on humans are carried out on patients who require the implantation of depth electrodes for
epilepsy monitoring or a DBS electrode to treat symptoms of Parkinson’s disease (PD) or
essential tremor (ET). We have already obtained some insights into neuronal mechanisms of
memory processes in MTL (concept cells, neurons for novelty/familiarity, etc.). Less human
studies were performed using the recordings of single-unit activity in the frontal lobe [67],
which is considered to be a crucial structure for our cognitive system. Here, the procedure
of implantation of DBS electrodes makes it possible to record neuronal activity from the
frontal cortex. As microelectrodes are often implanted as a part of standard procedure,
recordings from this area do not pose additional risk to patients. This approach was already
utilized by Ziv Williams group, showing that high quality neuronal recording can be
obtained from the dorsolateral prefrontal cortex (dlPFC). This resulted in finding specific
neurons that reflect subjective decisions [15] and neurons coding abstract rules [14]. The
approach can, therefore, be applied in order to investigate cognitive processes at the single-
neuronal level in PFC in order to obtain a broader view on cognition. Furthermore, we canBrain Sci. 2021, 11, 443 10 of 16
record the activity of neurons in all structures along the trajectory of the DBS implantation.
This includes several basal ganglia nuclei, such as the stratum or subthalamic nucleus.
Besides motor functions, these can have an important role in cognition [68–70].
Treatment with DBS is promising and it is already applied or considered in many other
diseases, like dystonia, obsessive-compulsive disorder, chronic pain, Alzheimer’s disease,
Tourette syndrome, autism, or other psychiatric disorders (such as treatment-resistant
depression, anorexia nervosa, mood disorders, addiction, schizophrenia, or anxiety dis-
orders) [71,72]. Using DBS as a treatment in other diseases will provide us with access to
many other brain areas, thus will create further research possibilities.
One of the aspects that holds back the research in the area of human single-neuron
recording is slow development of electrode technology. For instance, the so-called “Behnke-
Fried” electrode was introduced in the late 1990s and is still an accepted standard in
single-neuron recording during epilepsy monitoring more than 20 years later [13]. This
results from significant risks and effort associated with introducing new technologies in
the difficult environment of human intracranial recording. In animal electrophysiology
the main direction of electrode development took place in a silicone probe technology,
but because of the fact that these electrodes are at risk of breaking, their usage in human
brain is problematic. Still some new designs, either adapted from animal electrophysiology
or designed specifically for humans, are emerging. NeuroGrid is one of the designs
initially applied in animals, but successfully tested on humans [73,74]. These high-density,
large-scale flexible microgrids are able to record action potentials from the surface of the
cortex. Around of 840 mm2 of this grid can contain 240 electrodes, giving unprecedented
recording capabilities in humans. The other electrode successfully applied in human
electrophysiology is a linear probe [75,76]. Here, researchers embedded platinum-iridium
contacts in polyimide-epoxy composite in order to increase structural rigidity. Thanks to
multiple contacts, this electrode can record the activity of the whole human cortical column.
Additionally, because the cortical column is a basic computational unit of the brain, this
technology could be very useful in our endeavor to understand human cognition.
However, the electrodes described above are still only approved for single-case re-
search use. One new technology already approved for clinical use are microelectrode arrays
with sets of microwires implanted between ECoG electrodes [77]. Just like NeuroGrids, this
electrode has capabilities to record single neuron activity from the cortex surface, although
the number of contacts is much less impressive. Still, with this technology researchers
were able to record grid cells in the entorhinal cortex [77]. Another new electrode close to
being approved for humans combines a depth electrode used for epilepsy monitoring with
microwire tetrodes [78]. In this design, tetrodes protrude from the main electrode for up to
2 mm. In animal research, tetrodes are commonly used, because they dramatically improve
single-neuron isolation [79].
7. Human Single-Neuron Recordings of Pathological Brain
Human single-neuron recordings come with some limitations that we do not face
using noninvasive techniques, such as EEG or fMRI. The number of study subjects is
narrowed down to patients undergoing surgery due to brain pathology, e.g., [29,30,44].
What is more, brain coverage is limited as we can only implement electrodes in a few
brain areas, not necessarily the ones of our scientific interest, which might be unaffected
by the disease. However, possibly the greatest obstacle on our way to understanding
human cognition is the fact that we always investigate the pathological brain and, therefore,
potentially pathological activity.
Recording activity from the pathological brain may cast some doubts on the reliability
of neuronal activity we record, however, awareness of limitations could help us overcome
them and make reliable inferring. Since precise location of epileptic activity is unknown,
electrodes in drug resistant epilepsy are implanted in different brain areas. Some of them
are later classified as nonepileptic, which gives us access to undamaged tissue and we
do not record pathological activity [42,80]. Comparing effects observed in healthy andBrain Sci. 2021, 11, 443 11 of 16
pathological tissue is a useful tool to asses dependency of the results on epilepsy and in
general revealed little difference [29]. Findings in line with earlier animal studies and
evidence that confirms animal models give us reason to believe that recorded activity is
unchanged by pathology. Moreover, thanks to the fact that epilepsy is not a homogenous
disease [81], reproducible results from patients with different clinical pictures support the
view that findings observed in epilepsy patients are not likely to be related to pathological
changes in tissues, but rather represent physiological activity. As far as single-neuron
recordings in Parkinson’s disease (PD) patients are concerned, researchers often record
neurons from the substantia nigra (SN), which lies directly below the subthalamic nucleus,
the common DBS target in Parkinson’s disease [16,17,82–84]. While SN suffers from
massive loss of dopaminergic neurons in this disease, the loss is not uniform in the whole
SN and the loss in dorsal areas is more comparable with that observed in same age healthy
controls (unlike loss in the caudal part of SN pars compacta) [85]. As during the DBS
implantation the dorsal parts of SN are most accessible, we are able to record activity
from an area relatively untouched by disease. Furthermore we know from animal studies
that the dopaminergic neurons have longer spike waveforms compared to GABAergic
cells [86,87]. Neurons with longer spike waveforms were also observed in substantial
nigra recordings in humans suggesting the possibility to record dopaminergic cells in
PD [16,17]. Although we have to bear in mind that PD may influence the activity of the
remaining neurons, no correlation has been observed between spike waveform length and
PD stage [16]. Nevertheless, this issue remains open and requires further investigation.
Despite all their limitations, human single-neuron recordings create invaluable oppor-
tunities to record brain activity at its source and infer about direct neuronal mechanisms
that underlie behavior. We choose to perceive investigating the pathological brain not as a
limitation, but as an opportunity to gain knowledge on mechanisms behind pathology and
be able to develop more accurate therapies, or even find early biomarkers of pathological
activity.
Although cognitive impairment is not a feature hallmark in PD and ET diseases
(motor impairment mainly is), a significant number of patients do show poorer cognitive
performance than same age healthy controls. In PD and ET, we observe cognitive deficits in
attention, memory, speed processing, and executive abilities [88–91]. Moreover, PD and ET
patients face a greater risk of developing dementia than healthy controls [92,93]. It has been
calculated that about 50% of PD patients develop MCI during the first 10 years after their
diagnosis [94]. ET patients are also more likely to develop amnestic rather than nonamnestic
MCI. A study suggests that the problem lies not only with the retrieval of information
from memory but in the impairment of memory storage as such [95]. This shows that
despite the above disorders being predominantly motor diseases, they must be associated
with a factor or correlate that fosters cognitive impairment. The impairment profile is,
however, not homogenous. Although many patients display a cognitive decline, some
do not [93], and the cause of the disease is probably multifactorial [96], so defining what
causes this cognitive impairment in some patients and not the others is hard at this point.
This comes, however, as an opportunity to investigate the neuronal mechanism of cognitive
functions and hopefully understand the underlying mechanisms and elaborate accurate,
well addressed treatments drawing on the differences between cognitively impaired and
unimpaired brains within one disease. Due to its disruptive effect on the quality of life, we
can aim to study cognitive decline with single-neuron recordings. In the next paragraphs,
we suggest some examples of possible future research directions.
Patients with temporal lobe epilepsy (TLE) also suffer from memory decline as well
as widely reported deficits in recognition memory [97,98]. Some authors argue that there
are two separate processes behind recognition memory: (1) recollection, where a memory
is specifically retrieved via associations of details of an item or episode and (2) recog-
nition based on familiarity, where not associations but “feeling of oldness” comes into
play [97,98]. Indeed, several studies have shown that the former process is specifically
disrupted in TLE [97,99]. Human single-neuron recordings may help us determine which ofBrain Sci. 2021, 11, 443 12 of 16
the processes is actually disrupted in TLE and where specific treatment should be targeted.
Research using this recording technique already showed separate mechanisms in MTL that
are responsible for recognition memory: concept cells that form blocks of our cognition and
are a base for associative memory as well as specific cells that respond to novel vs. familiar
stimuli regardless of stimulus features (both discussed above in this review) [24,42]. An
analysis and comparison of the activity of these cells in epilepsy patients with TLE and with
epilepsy localized in other brain regions could provide us with direct evidence of which
mechanism of recognition memory is actually disrupted: recollection through associations
or recognition based on familiarity, or maybe both.
Intracranial EEG studies which focus on pathological changes in TLE relate interictal
epileptiform discharges (IED) with transient cognitive impairments [100–102]. Reed and
colleagues [102] conducted a study using recognition tasks and correlated behavioral
outcomes with activity of single neurons modulated by IED. When IED occurred just before
stimulus onset, the recognition of familiar images was impaired. Such disruption was not
present during recognition of novel stimuli. This finding supports the hypothesis that the
process of recognition based on familiarity is disrupted in epilepsy patients. However, we
still do not understand the exact mechanism behind this and future studies using human
single-neuron recordings could bring new insights into the matter.
Similarly, to TLE, recognition and associative memory is also impaired in PD [103]. As
we know from previous studies [16,42–44], neurons that respond to novelty vs. familiarity
are not only observed in MTL, but were found as well in the basal ganglia, where they
reinforce/weaken signals in the dynamic hippocampal-SN/VTA loop [40,41]. Neurode-
generation occurring in the basal ganglia (especially in the striatum and substantia nigra)
may impair the functioning of these neurons and the hippocampal-SN/VTA loop, and thus
cause a decline in recognition memory. It would be interesting to find out if a mechanism
similar to TLE occurs in PD. If so, we may be able to find a common base of cognitive
impairment in these motor diseases.
We believe that further studies using human single-neuron recordings may help us
understand the neural basis of cognition. The fact that human single-neuron recordings can
only be carried out on pathological brains, however limiting, comes as an invaluable op-
portunity to understand the mechanisms behind pathologies and lead to the development
of well-targeted new treatments.
8. Conclusions
Single-neuron recordings are an invaluable tool in brain investigation. Using this
technique, we can benefit from exceptional time and spatial resolution. So far, the majority
of studies using single-neuron recordings were conducted on animals, which, however
important, do not make it possible to grasp the complexity of human cognition. While
animal studies serve as a signpost for human studies and can be both valid and insightful,
the possibility of recording human brain using this technique is invaluable. Thanks to
human single-neuron recordings, scientists managed to discover concept cells, which,
to date, have not been found in other species and are probably unique to humans. We
discovered mechanisms that underlie memory processes and were able to gain some
insights into the neural basis of conscious perception and memory-based choices. Moreover,
access to pathological human brain creates an opportunity to investigate the mechanisms
behind pathologies and will, hopefully, enable the development of well-targeted therapies.
Last but not least, as intracranial implantation of electrodes carries a risk to the health
of the patients, it is worth drawing attention to the safety of the procedure. The goal of
understanding human cognition and designing well-targeted treatments for pathologies
is important, yet electrode implantation always involves the risk of internal bleeding or
damaging brain structures [104]. Therefore, there is a need to understand if implantation
of research electrodes carries additional risks. To date, studies conducted to address
this concern have showed that hybrid electrodes with microwires used for single-neuron
activity recordings do not increase the risk of the procedure. They are as safe as standardBrain Sci. 2021, 11, 443 13 of 16
electrodes [104,105] and combining them with intraoperative electrocorticography adds
minimal risk to surgery [106,107].
Author Contributions: Writing—first draft preparation, Z.R.K.; writing—review and editing, J.K.;
funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.
Funding: This review has been financially supported by the Polish National Science Centre NCN
Grant: 2019/34/E/HS6/00257 and by the BRAINCITY MAB/2018/10. The “Nencki-EMBL Center
of Excellence for Neural Plasticity and Brain Disorders: BRAINCITY” project is carried out within
the International Research Agendas programme of the Foundation for Polish Science cofinanced by
the European Union under the European Regional Development Fund.
Data Availability Statement: Data sharing not applicable.
Acknowledgments: The authors would like to thank Aneta Brzezicka, for her support and comments
on the manuscript.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
References
1. Baddeley, A.; Hitch, G. Working Memory. Psychol. Learn. Motiv. 1974, 8, 47–88. [CrossRef]
2. Cowan, N.; Elliott, E.M.; Saults, S.J.; Morey, C.C.; Mattox, S.; Hismjatullina, A.; Conway, A.R.A. On the capacity of attention: Its
estimation and its role in working memory and cognitive aptitudes. Cogn. Psychol. 2005, 51, 42–100. [CrossRef] [PubMed]
3. Li, D.; Christ, S.E.; Cowan, N. Domain-general and domain-specific functional networks in working memory. Neuroimage 2014,
102, 646–656. [CrossRef]
4. Oberauer, K. Access to Information in Working Memory: Exploring the Focus of Attention. J. Exp. Psychol. Learn. Mem. Cogn.
2002, 28, 411–421. [CrossRef] [PubMed]
5. Burle, B.; Spieser, L.; Roger, C.; Casini, L.; Hasbroucq, T.; Vidal, F. Spatial and temporal resolutions of EEG: Is it really black and
white? A scalp current density view. Int. J. Psychophysiol. 2015, 97, 210–220. [CrossRef]
6. Bandettini, P.A. Neuronal or hemodynamic? Grappling with the functional MRI signal. Brain Connect. 2014, 4, 487–498. [CrossRef]
7. Kim, S.G.; Ogawa, S. Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals. J. Cereb. Blood
Flow Metab. 2012, 32, 1188–1206. [CrossRef]
8. Logothetis, N.K. What we can do and what we cannot do with fMRI. Nature 2008, 453, 869–878. [CrossRef]
9. Buzsáki, G. The Brain from Inside Out; Oxford University Press: Oxford, UK, 2019.
10. Quian Quiroga, R. Plugging in to Human Memory: Advantages, Challenges, and Insights from Human Single-Neuron Recordings.
Cell 2019, 179, 1015–1032. [CrossRef] [PubMed]
11. Quian Quiroga, R.; Kraskov, A.; Koch, C.; Fried, I. Explicit Encoding of Multimodal Percepts by Single Neurons in the Human
Brain. Curr. Biol. 2009, 19, 1308–1313. [CrossRef] [PubMed]
12. Quian Quiroga, R.; Reddy, L.; Kreiman, G.; Koch, C.; Fried, I. Invariant visual representation by single neurons in the human
brain. Nature 2005, 435, 1102–1107. [CrossRef]
13. Fried, I.; Wilson, C.L.; Maidment, N.T.; Engel, J.; Behnke, E.; Fields, T.A.; Macdonald, K.A.; Morrow, J.W.; Ackerson, L. Cerebral
microdialysis combined with single-neuron and electroencephalographic recording in neurosurgical patients: Technical note. J.
Neurosurg. 1999, 91, 697–705. [CrossRef]
14. Mian, M.K.; Sheth, S.A.; Patel, S.R.; Spiliopoulos, K.; Eskandar, E.N.; Williams, Z.M. Encoding of rules by neurons in the human
dorsolateral prefrontal cortex. Cereb. Cortex 2014, 24, 807–816. [CrossRef]
15. Jamali, M.; Grannan, B.; Haroush, K.; Moses, Z.B.; Eskandar, E.N.; Herrington, T.; Patel, S.; Williams, Z.M. Dorsolateral prefrontal
neurons mediate subjective decisions and their variation in humans. Nat. Neurosci. 2019, 22, 1010–1020. [CrossRef]
16. Kamiński, J.; Mamelak, A.N.; Birch, K.; Mosher, C.P.; Tagliati, M.; Rutishauser, U. Novelty-Sensitive Dopaminergic Neurons in the
Human Substantia Nigra Predict Success of Declarative Memory Formation. Curr. Biol. 2018, 28, 1333–1343. [CrossRef] [PubMed]
17. Zaghloul, K.A.; Blanco, J.A.; Weidemann, C.T.; McGill, K.; Jaggi, J.L.; Baltuch, G.H.; Kahana, M.J. Human substantia nigra neurons
encode unexpected financial rewards. Science 2009, 323, 1496–1499. [CrossRef]
18. Rutishauser, U.; Aflalo, T.; Rosario, E.R.; Pouratian, N.; Andersen, R.A. Single-Neuron Representation of Memory Strength and
Recognition Confidence in Left Human Posterior Parietal Cortex. Neuron 2018, 97, 209–220. [CrossRef] [PubMed]
19. Aflalo, T.; Zhang, C.Y.; Rosario, E.R.; Pouratian, N.; Orban, G.A.; Andersen, R.A. A shared neural substrate for action verbs and
observed actions in human posterior parietal cortex. Sci. Adv. 2020, 6, eabb3984. [CrossRef] [PubMed]
20. Rey, H.G.; Pedreira, C.; Quian Quiroga, R. Past, present and future of spike sorting techniques. Brain Res. Bull. 2015, 119, 106–117.
[CrossRef]
21. Gross, C.G. Genealogy of the “grandmother cell”. Neuroscientist 2002, 8, 512–518. [CrossRef] [PubMed]
22. Quian Quiroga, R.; Fried, I.; Koch, C. Brain cells for grandmother. Sci. Am. 2013, 308, 30–35. [CrossRef]Brain Sci. 2021, 11, 443 14 of 16
23. Konorski, J. Integrative Activity of the Brain; University of Chicago Press: Chicago, IL, USA, 1967.
24. Quian Quiroga, R. Concept cells: The building blocks of declarative memory functions. Nat. Rev. Neurosci. 2012, 13, 587–597.
[CrossRef]
25. De Falco, E.; Ison, M.J.; Fried, I.; Quian Quiroga, R. Long-term coding of personal and universal associations underlying the
memory web in the human brain. Nat. Commun. 2016, 7, 1–11. [CrossRef]
26. Quian Quiroga, R.; Kraskov, A.; Mormann, F.; Fried, I.; Koch, C. Single-Cell Responses to Face Adaptation in the Human Medial
Temporal Lobe. Neuron 2014, 84, 363–369. [CrossRef] [PubMed]
27. Reber, T.P.; Faber, J.; Niediek, J.; Bostro, J.; Elger, C.E.; Mormann, F.; Elger, C.E.; Mormann, F. Single-Neuron Correlates of
Conscious Perception in the Human Medial Temporal Lobe Report Single-Neuron Correlates of Conscious Perception in the
Human Medial Temporal Lobe. Curr. Biol. 2017, 27, 1–8. [CrossRef]
28. Ison, M.J.; Quian Quiroga, R.; Fried, I. Rapid Encoding of New Memories by Individual Neurons in the Human Brain. Neuron
2015, 87, 220–230. [CrossRef]
29. Kamiński, J.; Sullivan, S.; Chung, J.M.; Ross, I.B.; Mamelak, A.N.; Rutishauser, U. Persistently active neurons in human medial
frontal and medial temporal lobe support working memory. Nat. Neurosci. 2017, 20, 590–601. [CrossRef] [PubMed]
30. Kornblith, S.; Quian Quiroga, R.; Koch, C.; Fried, I.; Mormann, F. Persistent Single-Neuron Activity during Working Memory in
the Human Medial Temporal Lobe. Curr. Biol. 2017, 27, 1026–1032. [CrossRef] [PubMed]
31. Corkin, S. What’s new with the amnesic patient H.M.? Nat. Rev. Neurosci. 2002, 3, 153–160. [CrossRef]
32. Squire, L.R.; Stark, C.E.L.; Clark, R.E. The medial temporal lobe. Annu. Rev. Neurosci. 2004, 27, 279–306. [CrossRef]
33. Squire, L.R.; Zola-Morgan, S. The medial temporal lobe memory system. Science 1991, 253, 1380–1386. [CrossRef] [PubMed]
34. Tulving, E.; Markowitsch, H.J. Episodic and declarative memory: Role of the hippocampus. Hippocampus 1998, 8, 198–204.
[CrossRef]
35. Eichenbaum, H.; Yonelinas, A.P.; Ranganath, C. The medial temporal lobe and recognition memory. Annu. Rev. Neurosci. 2007, 30,
123–152. [CrossRef]
36. Borders, A.A.; Aly, M.; Parks, C.M.; Yonelinas, A.P. The hippocampus is particularly important for building associations across
stimulus domains. Neuropsychologia 2017, 99, 335–342. [CrossRef]
37. Brasted, P.J.; Bussey, T.J.; Murray, E.A.; Wise, S.P. Role of the hippocampal system in associative learning beyond the spatial
domain. Brain 2003, 126, 1202–1223. [CrossRef]
38. Mayes, A.; Montaldi, D.; Migo, E. Associative memory and the medial temporal lobes. Trends Cogn. Sci. 2007, 11, 126–135.
[CrossRef] [PubMed]
39. Scoville, W.B.; Milner, R.B. Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 1957, 20,
11–21. [CrossRef] [PubMed]
40. Lisman, J.E.; Grace, A.A. The hippocampal-VTA loop: Controlling the entry of information into long-term memory. Neuron 2005,
46, 703–713. [CrossRef] [PubMed]
41. Lisman, J.; Grace, A.A.; Duzel, E. A neoHebbian framework for episodic memory; role of dopamine-dependent late LTP. Trends
Neurosci. 2011, 34, 536–547. [CrossRef]
42. Rutishauser, U.; Mamelak, A.N.; Schuman, E.M. Single-trial learning of novel stimuli by individual neurons of the human
hippocampus-amygdala complex. Neuron 2006, 49, 805–813. [CrossRef]
43. Rutishauser, U.; Schuman, E.M.; Mamelak, A.N. Activity of human hippocampal and amygdala neurons during retrieval of
declarative memories. Proc. Natl. Acad. Sci. USA 2008, 105, 11032. [CrossRef] [PubMed]
44. Rutishauser, U.; Ross, I.B.; Mamelak, A.N.; Schuman, E.M. Human memory strength is predicted by theta-frequency phase-locking
of single neurons. Nature 2010, 464, 903–907. [CrossRef] [PubMed]
45. Nieoullon, A. Dopamine and the regulation of cognition and attention. Prog. Neurobiol. 2002, 67, 53–83. [CrossRef]
46. Standing, L.; Conezio, J.; Haber, R.N. Perception and memory for pictures: Single-trial learning of 2500 visual stimuli. Psychon.
Sci. 1970, 19, 73–74. [CrossRef]
47. Atkinson, R.C.; Shiffrin, R.M. Human Memory: A proposed system and its control processes BT—The Psychology of Learning
and Motivation. Psychol. Learn. Motiv. 1968, 2, 89–195.
48. Oberauer, K. Working Memory and Attention—A Conceptual Analysis and Review. J. Cogn. 2019, 2, 1–23. [CrossRef]
49. Baddeley, A. Working memory: Theories, models, and controversies. Annu. Rev. Psychol. 2012, 63, 1–29. [CrossRef]
50. Fuster, J.M.; Alexander, G.E. Neuron Activity related to short-term memory. Science 1971, 173, 652–654. [CrossRef]
51. Constantinidis, C.; Funahashi, S.; Lee, D.; Murray, J.D.; Qi, X.L.; Wang, M.; Arnsten, A.F.T. Persistent spiking activity underlies
working memory. J. Neurosci. 2018, 38, 7020–7028. [CrossRef]
52. Olshausen, B.A.; Field, D.J. Sparse coding of sensory inputs. Curr. Opin. Neurobiol. 2004, 14, 481–487. [CrossRef] [PubMed]
53. Compte, A.; Brunel, N.; Goldman-Rakic, P.S.; Wang, X.J. Synaptic mechanisms and network dynamics underlying spatial working
memory in a cortical network model. Cereb. Cortex 2000, 10, 910–923. [CrossRef]
54. Kamiński, J.; Rutishauser, U. Between persistently active and activity-silent frameworks: Novel vistas on the cellular basis of
working memory. Ann. N. Y. Acad. Sci. 2020, 1464, 64–75. [CrossRef]
55. Jeneson, A.; Squire, L.R. Working memory, long-term memory, and medial temporal lobe function. Learn. Mem. 2012, 19, 15–25.
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